The present technology relates to electronic design automation tools, and processes for improving utilization of computing resources required in such tools.
Electronic design automation EDA tools include a class of physical simulation tools used for investigating at a device scale, the properties of materials and the performance of devices made using such materials. Representative physical simulation tools include Sentaurus™ provided by Synopsys, Inc. in Mountain View, Calif. Such tools are optimized to model devices based on well-known materials, such as silicon based materials, for which long experience has resulted in good understanding of the physical parameters needed to simulation.
It is desirable to extend the processes of such tools to materials for which the physical parameters are not well understood, and to new materials.
Atomistic scale models can be used to characterize materials at an atomic level. Such models can produce data needed to estimate the physical parameters of materials with minimal input data that characterizes a set of atoms, and defects in the set of atoms.
Atomistic scale models include density functional theory (DFT) and molecular dynamics (MD) classes of models. These models are based on ab initio, or first principles, calculations of electronic structures based on quantum physics theories. First principles models can be used to compute thermodynamic and transport properties of pure materials, defects and dopants.
Physical parameters needed by kinetic Monte Carlo (KMC) and continuum calculations, such as those used in the Sentaurus product, can be derived from the results of first principles calculations, which in turn can be used to derive device properties. Thus, first principles models are a potential tool for determining estimates of parameters usable for physical simulation based on new materials and other materials that are not already well characterized.
These processes have very intensive computing resource requirements, in terms of time for execution and the processor power needed. It is desirable to provide technologies that can optimize utilization of the computing resources.
The accuracy of first principles calculations depends on the input variables for approximation such as cell size, cut-off energy, k-points, and so on. As each iteration of first principles calculation typically requires a considerable amount of computing resources (e.g., CPU time), trial and error approaches with various approximations are often used. As a result of the complexities of iterations, performing first principles calculations is difficult. First principles calculations can require in-depth understanding of quantum physics and related theories, and can take a person significant amount of time to understand the calculations. Also, it can require manual work to extract physical parameters from results of the first principles calculations.
It is thus desirable to provide a method to automatically execute first principles calculations based on user input, analyze results from the first principles calculations, and extract physical parameters from the results for use in device scale simulation processes, including KMC and continuum calculations.